Скачать или смотреть How to Assign Values from a Dictionary to a New Column in Pandas DataFrame

Learn how to efficiently add a new column to your Pandas DataFrame based on a dictionary using Python. This guide walks you through the steps to handle both lists and strings as values in the dictionary.
---
This video is based on the question https://stackoverflow.com/q/67336122/ asked by the user 'Steve Kundukulangara' ( https://stackoverflow.com/u/15565320/ ) and on the answer https://stackoverflow.com/a/67336236/ provided by the user 'ALollz' ( https://stackoverflow.com/u/4333359/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Assign values from a dictionary to a new column based on condition

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
How to Assign Values from a Dictionary to a New Column in Pandas DataFrame

In data manipulation with Python, specifically using the Pandas library, it's common to encounter scenarios where you want to enrich your existing data. One such task is adding a new column to a Pandas DataFrame based on the values of a dictionary. This guide will guide you through the process step by step, ensuring that you understand how to handle different data types effectively.

The Problem

Let’s say you have a DataFrame containing city names and their corresponding sales figures. For instance:

CitySalesSan Diego500Texas400Nebraska300Macau200Rome100London50Manchester70Now, you want to add another column to this DataFrame to indicate the country for each city. The countries are stored in a dictionary as follows:

[[See Video to Reveal this Text or Code Snippet]]

The desired output would look like this:

CitySalesCountrySan Diego500USTexas400USNebraska300USMacau200Hong KongRome100ItalyLondon50EnglandManchester70EnglandThe Solution

To achieve this, we need to:

Flatten the Dictionary: Convert entries in the dictionary that contain lists to key-value pairs where the list items are assigned the same key.

Map the Values: Use this flattened dictionary to map countries to their respective cities and add them as a new column in the DataFrame.

Step 1: Flatten the Dictionary

We will create a function called flatten_dict to handle this task. It will:

Iterate through the dictionary.

Check if the value is a list or a single string.

For lists, assign the key to each item in the list, while for strings, simply assign the key to that string.

Here’s the implementation:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Create the New Column

Now, we'll create a flattened dictionary and use it to assign the country to a new column in our DataFrame.

[[See Video to Reveal this Text or Code Snippet]]

Complete Code Example

Here’s how your complete code should look:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

By following the steps outlined above, you can efficiently assign values from a dictionary to a new column in your Pandas DataFrame based on city names, handling both lists and strings. This method not only keeps your data organized but also enhances your ability to analyze and visualize data accurately.

With increasing complexity in data manipulation, mastering these techniques is essential for any data analyst or developer. Happy coding!